Ordination

Loading Packages and Preparing Data

library("phyloseq"); packageVersion("phyloseq")

## [1] '1.32.0'

data(GlobalPatterns)
library("ggplot2"); packageVersion("ggplot2")

## [1] '3.3.2'

library("plyr"); packageVersion("plyr")

## [1] '1.8.6'

theme_set(theme_bw())

# Removing OTUs that do not show appear more than 5 times in more than half the samples
GP = GlobalPatterns
wh0 = genefilter_sample(GP, filterfun_sample(function(x) x > 5), A=0.5*nsamples(GP))
GP1 = prune_taxa(wh0, GP)
# Transform to even sampling depth
GP1 = transform_sample_counts(GP1, function(x) 1E6 * x/sum(x))
# Keep only the most abundant five phyla
phylum.sum = tapply(taxa_sums(GP1), tax_table(GP1)[, "Phylum"], sum, na.rm=TRUE)
top5phyla = names(sort(phylum.sum, TRUE))[1:5]
GP1 = prune_taxa((tax_table(GP1)[, "Phylum"] %in% top5phyla), GP1)
# Define a human-associated versus non-human categorical variable:
human = get_variable(GP1, "SampleType") %in% c("Feces", "Mock", "Skin", "Tongue")
sample_data(GP1)$human <- factor(human)

Four Main Ordination Plots

# 1) Just OTUs
GP.ord <- ordinate(GP1, "NMDS", "bray")

## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1333468 
## Run 1 stress 0.1569043 
## Run 2 stress 0.1808227 
## Run 3 stress 0.1660422 
## Run 4 stress 0.1570722 
## Run 5 stress 0.1639658 
## Run 6 stress 0.307523 
## Run 7 stress 0.1570722 
## Run 8 stress 0.1333468 
## ... New best solution
## ... Procrustes: rmse 1.819151e-06  max resid 4.956866e-06 
## ... Similar to previous best
## Run 9 stress 0.1633801 
## Run 10 stress 0.1518734 
## Run 11 stress 0.1449078 
## Run 12 stress 0.1518733 
## Run 13 stress 0.1527459 
## Run 14 stress 0.1699695 
## Run 15 stress 0.1333468 
## ... Procrustes: rmse 3.107573e-06  max resid 9.333751e-06 
## ... Similar to previous best
## Run 16 stress 0.1333468 
## ... Procrustes: rmse 1.969927e-06  max resid 4.474892e-06 
## ... Similar to previous best
## Run 17 stress 0.1528906 
## Run 18 stress 0.1659803 
## Run 19 stress 0.1391747 
## Run 20 stress 0.1660647 
## *** Solution reached

p1 = plot_ordination(GP1, GP.ord, type="taxa", color="Phylum", title="taxa")
print(p1)

# Facetting
p1 + facet_wrap(~Phylum, 3)

# 2) Just Samples
p2 = plot_ordination(GP1, GP.ord, type="samples", color="SampleType", shape="human") 
p2 + geom_polygon(aes(fill=SampleType)) + geom_point(size=5) + ggtitle("samples")

# 3) Biplot graphic
p3 = plot_ordination(GP1, GP.ord, type="biplot", color="SampleType", shape="Phylum", title="biplot")
# Some stuff to modify the automatic shape scale
GP1.shape.names = get_taxa_unique(GP1, "Phylum")
GP1.shape <- 15:(15 + length(GP1.shape.names) - 1)
names(GP1.shape) <- GP1.shape.names
GP1.shape["samples"] <- 16
p3 + scale_shape_manual(values=GP1.shape)

# 4) Split Graphic
p4 = plot_ordination(GP1, GP.ord, type="split", color="Phylum", shape="human", label="SampleType", title="split") 
p4

# The following function reproduces ggplot2’s default color scale. 
gg_color_hue <- function(n){
    hues = seq(15, 375, length=n+1)
    hcl(h=hues, l=65, c=100)[1:n]
}
color.names <- levels(p4$data$Phylum)
p4cols <- gg_color_hue(length(color.names))
names(p4cols) <- color.names
p4cols["samples"] <- "black"
p4 + scale_color_manual(values=p4cols)

Supported Ordination Methods

dist = "bray"
ord_meths = c("DCA", "CCA", "RDA", "DPCoA", "NMDS", "MDS", "PCoA")
plist = llply(as.list(ord_meths), function(i, physeq, dist){
        ordi = ordinate(physeq, method=i, distance=dist)
        plot_ordination(physeq, ordi, "samples", color="SampleType")
}, GP1, dist)

## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1333468 
## Run 1 stress 0.1449079 
## Run 2 stress 0.1333468 
## ... Procrustes: rmse 1.800118e-06  max resid 5.216598e-06 
## ... Similar to previous best
## Run 3 stress 0.161005 
## Run 4 stress 0.1497848 
## Run 5 stress 0.1635766 
## Run 6 stress 0.1498724 
## Run 7 stress 0.1448829 
## Run 8 stress 0.1333468 
## ... Procrustes: rmse 1.184031e-06  max resid 2.412965e-06 
## ... Similar to previous best
## Run 9 stress 0.2536105 
## Run 10 stress 0.1502708 
## Run 11 stress 0.1465222 
## Run 12 stress 0.1529686 
## Run 13 stress 0.1391747 
## Run 14 stress 0.2819405 
## Run 15 stress 0.1529609 
## Run 16 stress 0.1502708 
## Run 17 stress 0.1469145 
## Run 18 stress 0.1477687 
## Run 19 stress 0.1585099 
## Run 20 stress 0.18208 
## *** Solution reached

names(plist) <- ord_meths

pdataframe = ldply(plist, function(x){
    df = x$data[, 1:2]
    colnames(df) = c("Axis_1", "Axis_2")
    return(cbind(df, x$data))
})
names(pdataframe)[1] = "method"

p = ggplot(pdataframe, aes(Axis_1, Axis_2, color=SampleType, shape=human, fill=SampleType))
p = p + geom_point(size=4) + geom_polygon()
p = p + facet_wrap(~method, scales="free")
p = p + scale_fill_brewer(type="qual", palette="Set1")
p = p + scale_colour_brewer(type="qual", palette="Set1")
p

plist[[2]]

p = plist[[2]] + scale_colour_brewer(type="qual", palette="Set1")
p = p + scale_fill_brewer(type="qual", palette="Set1")
p = p + geom_point(size=5) + geom_polygon(aes(fill=SampleType))
p

MDS (“PCoA”) on Unifrac Distances

ordu = ordinate(GP1, "PCoA", "unifrac", weighted=TRUE)
plot_ordination(GP1, ordu, color="SampleType", shape="human")

p = plot_ordination(GP1, ordu, color="SampleType", shape="human")
p = p + geom_point(size=7, alpha=0.75)
p = p + scale_colour_brewer(type="qual", palette="Set1")
p + ggtitle("MDS/PCoA on weighted-UniFrac distance, GlobalPatterns")

Alpha Diversity graphics

Load packages and Preparing Data

library("phyloseq"); packageVersion("phyloseq")

## [1] '1.32.0'

data("GlobalPatterns")
library("ggplot2"); packageVersion("ggplot2")

## [1] '3.3.2'

theme_set(theme_bw())
pal = "Set1"
scale_colour_discrete <-  function(palname=pal, ...){
  scale_colour_brewer(palette=palname, ...)
}
scale_fill_discrete <-  function(palname=pal, ...){
  scale_fill_brewer(palette=palname, ...)
}

Prepare data

GP <- prune_species(speciesSums(GlobalPatterns) > 0, GlobalPatterns)

## Warning: 'prune_species' is deprecated.
## Use 'prune_taxa' instead.
## See help("Deprecated") and help("phyloseq-deprecated").

## Warning: 'speciesSums' is deprecated.
## Use 'taxa_sums' instead.
## See help("Deprecated") and help("phyloseq-deprecated").

Plot examples

plot_richness(GP)

plot_richness(GP, measures=c("Chao1", "Shannon"))

plot_richness(GP, x="SampleType", measures=c("Chao1", "Shannon"))

plot_richness(GP, x="SampleType", measures=c("Chao1", "Shannon"))

sampleData(GP)$human <- getVariable(GP, "SampleType") %in% c("Feces", "Mock", "Skin", "Tongue")

## Warning: 'getVariable' is deprecated.
## Use 'get_variable' instead.
## See help("Deprecated") and help("phyloseq-deprecated").

## Warning: 'sampleData' is deprecated.
## Use 'sample_data' instead.
## See help("Deprecated") and help("phyloseq-deprecated").

## Warning: 'sampleData<-' is deprecated.
## Use 'sample_data<-' instead.
## See help("Deprecated") and help("phyloseq-deprecated").

plot_richness(GP, x="human", color="SampleType", measures=c("Chao1", "Shannon"))

GPst = merge_samples(GP, "SampleType")
# repair variables that were damaged during merge (coerced to numeric)
sample_data(GPst)$SampleType <- factor(sample_names(GPst))
sample_data(GPst)$human <- as.logical(sample_data(GPst)$human)

p = plot_richness(GPst, x="human", color="SampleType", measures=c("Chao1", "Shannon"))
p + geom_point(size=5, alpha=0.7)

More detail about ggplot2

p$layers

## [[1]]
## geom_point: na.rm = TRUE
## stat_identity: na.rm = TRUE
## position_identity 
## 
## [[2]]
## mapping: ymax = ~value + se, ymin = ~value - se 
## geom_errorbar: na.rm = FALSE, orientation = NA, width = 0.1, width = 0.1, flipped_aes = FALSE
## stat_identity: na.rm = FALSE
## position_identity

p$layers <- p$layers[-1]
p + geom_point(size=5, alpha=0.7)

Heatmaps

Load Packages and Preparing Data

library("phyloseq"); packageVersion("phyloseq")

## [1] '1.32.0'

library("ggplot2"); packageVersion("ggplot2")

## [1] '3.3.2'

theme_set(theme_bw())

Plot a 300-taxa dataset

data("GlobalPatterns")
gpt <- subset_taxa(GlobalPatterns, Kingdom=="Bacteria")
gpt <- prune_taxa(names(sort(taxa_sums(gpt),TRUE)[1:300]), gpt)
plot_heatmap(gpt, sample.label="SampleType")

## Warning: Transformation introduced infinite values in discrete y-axis

Subset a smaller dataset based on an Archaeal phylum

gpac <- subset_taxa(GlobalPatterns, Phylum=="Crenarchaeota")

Default plot_heatmap settings

plot_heatmap(gpac)

## Warning: Transformation introduced infinite values in discrete y-axis

Re-label by a sample variable and taxonomic family

(p <- plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family"))

## Warning: Transformation introduced infinite values in discrete y-axis

Re-label axis titles

p$scales$scales[[1]]$name <- "My X-Axis"
p$scales$scales[[2]]$name <- "My Y-Axis"
print(p)

## Warning: Transformation introduced infinite values in discrete y-axis

* Repeat the plot and Change the color scheme

plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#000033", high="#CCFF66")

## Warning: Transformation introduced infinite values in discrete y-axis

# Dark-blue to red scheme
plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#000033", high="#FF3300")

## Warning: Transformation introduced infinite values in discrete y-axis

# Dark-blue to light blue scheme
plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#000033", high="#66CCFF")

## Warning: Transformation introduced infinite values in discrete y-axis

# Dark on light scheme
plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#66CCFF", high="#000033", na.value="white")

## Warning: Transformation introduced infinite values in discrete y-axis

plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#FFFFCC", high="#000033", na.value="white")

## Warning: Transformation introduced infinite values in discrete y-axis

Different ordination methods, distances

plot_heatmap(gpac, "NMDS", "jaccard")

## Warning: Transformation introduced infinite values in discrete y-axis

plot_heatmap(gpac, "DCA", "none", "SampleType", "Family")

## Warning: Transformation introduced infinite values in discrete y-axis

plot_heatmap(gpac, "RDA", "none", "SampleType", "Family")

## Warning: Transformation introduced infinite values in discrete y-axis

plot_heatmap(gpac, "PCoA", "bray", "SampleType", "Family")

## Warning: Transformation introduced infinite values in discrete y-axis

plot_heatmap(gpac, "PCoA", "unifrac", "SampleType", "Family")

## Warning: Transformation introduced infinite values in discrete y-axis

plot_heatmap(gpac, "MDS", "unifrac", "SampleType", "Family", weighted=TRUE)

## Warning: Transformation introduced infinite values in discrete y-axis

heatmap(otu_table(gpac))

Networks

Load packages and Preparing Data

library(phyloseq); packageVersion("phyloseq")

## [1] '1.32.0'

packageVersion("ggplot2")

## [1] '3.3.2'

data(enterotype)

# For complete reproducibility of the images produced later in this tutorial, it is possible to set the random number generator seed explicitly:
set.seed(711L)
# Because we want to use the enterotype designations as a plot feature in these plots, we need to remove the 9 samples for which no enterotype designation was assigned 
enterotype = subset_samples(enterotype, !is.na(Enterotype))

Plot_net function

plot_net(enterotype, maxdist = 0.4, point_label = "Sample_ID")

plot_net(enterotype, maxdist = 0.3, color = "SeqTech", shape="Enterotype")

The plot_network function

ig <- make_network(enterotype, max.dist=0.3)

plot_network(ig, enterotype)

plot_network(ig, enterotype, color="SeqTech", shape="Enterotype", line_weight=0.4, label=NULL)

ig <- make_network(enterotype, max.dist=0.2)
plot_network(ig, enterotype, color="SeqTech", shape="Enterotype", line_weight=0.4, label=NULL)

ig <- make_network(enterotype, dist.fun="bray", max.dist=0.3)
plot_network(ig, enterotype, color="SeqTech", shape="Enterotype", line_weight=0.4, label=NULL)

Application written in R [@RCoreTeam] using the Shiny framework [@Chang2015]. # REFERENCES

Lab11X

Jessica Atuahene

12/11/2020

Ordination

Alpha Diversity graphics

Heatmaps

Networks